Overview

Dataset statistics

Number of variables13
Number of observations53
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 KiB
Average record size in memory139.2 B

Variable types

Text1
Numeric12

Alerts

CochesVendidos_2017 is highly overall correlated with CochesVendidos_2018 and 4 other fieldsHigh correlation
CochesVendidos_2018 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2019 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2020 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2021 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2022 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
PIB_2017 is highly overall correlated with PIB_2018 and 4 other fieldsHigh correlation
PIB_2018 is highly overall correlated with PIB_2017 and 4 other fieldsHigh correlation
PIB_2019 is highly overall correlated with PIB_2017 and 4 other fieldsHigh correlation
PIB_2020 is highly overall correlated with PIB_2017 and 4 other fieldsHigh correlation
PIB_2021 is highly overall correlated with PIB_2017 and 4 other fieldsHigh correlation
PIB_2022 is highly overall correlated with PIB_2017 and 4 other fieldsHigh correlation
Country has unique valuesUnique
CochesVendidos_2017 has unique valuesUnique
CochesVendidos_2018 has unique valuesUnique
CochesVendidos_2019 has unique valuesUnique
CochesVendidos_2020 has unique valuesUnique
PIB_2017 has unique valuesUnique
PIB_2018 has unique valuesUnique
PIB_2019 has unique valuesUnique
PIB_2020 has unique valuesUnique
PIB_2021 has unique valuesUnique
PIB_2022 has unique valuesUnique
CochesVendidos_2020 has 1 (1.9%) zerosZeros
CochesVendidos_2021 has 2 (3.8%) zerosZeros
CochesVendidos_2022 has 2 (3.8%) zerosZeros

Reproduction

Analysis started2023-11-24 18:53:50.023902
Analysis finished2023-11-24 18:54:05.824113
Duration15.8 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Country
Text

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-11-24T19:54:05.944071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.6792453
Min length4

Characters and Unicode

Total characters407
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)100.0%

Sample

1st rowArgentina
2nd rowAustralia
3rd rowAustria
4th rowBelgium
5th rowBrazil
ValueCountFrequency (%)
argentina 1
 
1.7%
denmark 1
 
1.7%
australia 1
 
1.7%
kazakhstan 1
 
1.7%
austria 1
 
1.7%
belgium 1
 
1.7%
brazil 1
 
1.7%
bulgaria 1
 
1.7%
canada 1
 
1.7%
chile 1
 
1.7%
Other values (48) 48
82.8%
2023-11-24T19:54:06.292321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 60
14.7%
i 34
 
8.4%
n 33
 
8.1%
e 30
 
7.4%
r 25
 
6.1%
l 20
 
4.9%
t 17
 
4.2%
o 16
 
3.9%
u 15
 
3.7%
d 12
 
2.9%
Other values (37) 145
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 344
84.5%
Uppercase Letter 58
 
14.3%
Space Separator 5
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 60
17.4%
i 34
9.9%
n 33
9.6%
e 30
 
8.7%
r 25
 
7.3%
l 20
 
5.8%
t 17
 
4.9%
o 16
 
4.7%
u 15
 
4.4%
d 12
 
3.5%
Other values (14) 82
23.8%
Uppercase Letter
ValueCountFrequency (%)
S 7
12.1%
A 5
 
8.6%
P 5
 
8.6%
C 5
 
8.6%
I 5
 
8.6%
N 3
 
5.2%
R 3
 
5.2%
M 3
 
5.2%
U 3
 
5.2%
B 3
 
5.2%
Other values (12) 16
27.6%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 402
98.8%
Common 5
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 60
14.9%
i 34
 
8.5%
n 33
 
8.2%
e 30
 
7.5%
r 25
 
6.2%
l 20
 
5.0%
t 17
 
4.2%
o 16
 
4.0%
u 15
 
3.7%
d 12
 
3.0%
Other values (36) 140
34.8%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 60
14.7%
i 34
 
8.4%
n 33
 
8.1%
e 30
 
7.4%
r 25
 
6.1%
l 20
 
4.9%
t 17
 
4.2%
o 16
 
3.9%
u 15
 
3.7%
d 12
 
2.9%
Other values (37) 145
35.6%

CochesVendidos_2017
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean652735.81
Minimum7801
Maximum4973577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-11-24T19:54:06.469907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7801
5-th percentile26157.6
Q1104116
median251763
Q3578146
95-th percentile2584131.2
Maximum4973577
Range4965776
Interquartile range (IQR)474030

Descriptive statistics

Standard deviation984593.94
Coefficient of variation (CV)1.5084111
Kurtosis7.2296124
Mean652735.81
Median Absolute Deviation (MAD)171274
Skewness2.5522547
Sum34594998
Variance9.6942522 × 1011
MonotonicityNot monotonic
2023-11-24T19:54:06.637682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
859770 1
 
1.9%
50495 1
 
1.9%
1508843 1
 
1.9%
7801 1
 
1.9%
386442 1
 
1.9%
145119 1
 
1.9%
177900 1
 
1.9%
140132 1
 
1.9%
239725 1
 
1.9%
434691 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
7801 1
1.9%
24223 1
1.9%
24834 1
1.9%
27040 1
1.9%
44264 1
1.9%
50165 1
1.9%
50495 1
1.9%
70304 1
1.9%
80489 1
1.9%
83991 1
1.9%
ValueCountFrequency (%)
4973577 1
1.9%
3419716 1
1.9%
2800661 1
1.9%
2439778 1
1.9%
2169110 1
1.9%
2037877 1
1.9%
1876296 1
1.9%
1508843 1
1.9%
1372519 1
1.9%
1122073 1
1.9%

CochesVendidos_2018
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean664641.43
Minimum16791
Maximum5014383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-11-24T19:54:06.802371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16791
5-th percentile34515.2
Q1122849
median254560
Q3579262
95-th percentile2682780.8
Maximum5014383
Range4997592
Interquartile range (IQR)456413

Descriptive statistics

Standard deviation1001379.9
Coefficient of variation (CV)1.5066468
Kurtosis6.9792692
Mean664641.43
Median Absolute Deviation (MAD)160358
Skewness2.5355851
Sum35225996
Variance1.0027616 × 1012
MonotonicityNot monotonic
2023-11-24T19:54:06.970824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
772980 1
 
1.9%
52426 1
 
1.9%
1396939 1
 
1.9%
16791 1
 
1.9%
410003 1
 
1.9%
146197 1
 
1.9%
168174 1
 
1.9%
122849 1
 
1.9%
254560 1
 
1.9%
378354 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
16791 1
1.9%
25581 1
1.9%
30218 1
1.9%
37380 1
1.9%
41650 1
1.9%
52426 1
1.9%
58557 1
1.9%
72013 1
1.9%
82917 1
1.9%
88547 1
1.9%
ValueCountFrequency (%)
5014383 1
1.9%
3428367 1
1.9%
2928332 1
1.9%
2519080 1
1.9%
2475994 1
1.9%
1971108 1
1.9%
1832162 1
1.9%
1478681 1
1.9%
1396939 1
1.9%
1076319 1
1.9%

CochesVendidos_2019
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean651419.17
Minimum20764
Maximum4934557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-11-24T19:54:07.131749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20764
5-th percentile35687.2
Q1116651
median250291
Q3537814
95-th percentile2618163.8
Maximum4934557
Range4913793
Interquartile range (IQR)421163

Descriptive statistics

Standard deviation994103.35
Coefficient of variation (CV)1.5260579
Kurtosis7.0562478
Mean651419.17
Median Absolute Deviation (MAD)165445
Skewness2.5753495
Sum34525216
Variance9.8824148 × 1011
MonotonicityNot monotonic
2023-11-24T19:54:07.301692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
436794 1
 
1.9%
53656 1
 
1.9%
1288922 1
 
1.9%
20764 1
 
1.9%
415736 1
 
1.9%
139354 1
 
1.9%
162173 1
 
1.9%
108257 1
 
1.9%
186588 1
 
1.9%
387845 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
20764 1
1.9%
26839 1
1.9%
31726 1
1.9%
38328 1
1.9%
47274 1
1.9%
53656 1
1.9%
61707 1
1.9%
71850 1
1.9%
81875 1
1.9%
90908 1
1.9%
ValueCountFrequency (%)
4934557 1
1.9%
3593854 1
1.9%
2679239 1
1.9%
2577447 1
1.9%
2570268 1
1.9%
1966372 1
1.9%
1772639 1
1.9%
1415709 1
1.9%
1288922 1
1.9%
983031 1
1.9%

CochesVendidos_2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean509282.25
Minimum0
Maximum4375857
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-11-24T19:54:07.465936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20479
Q187090
median211601
Q3412650
95-th percentile2024941.2
Maximum4375857
Range4375857
Interquartile range (IQR)325560

Descriptive statistics

Standard deviation822975.32
Coefficient of variation (CV)1.6159513
Kurtosis9.7929509
Mean509282.25
Median Absolute Deviation (MAD)135114
Skewness2.9345611
Sum26991959
Variance6.7728838 × 1011
MonotonicityNot monotonic
2023-11-24T19:54:07.626777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
324340 1
 
1.9%
45323 1
 
1.9%
927577 1
 
1.9%
15544 1
 
1.9%
322283 1
 
1.9%
106059 1
 
1.9%
151411 1
 
1.9%
69534 1
 
1.9%
123403 1
 
1.9%
226673 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
0 1
1.9%
15544 1
1.9%
18697 1
1.9%
21667 1
1.9%
32477 1
1.9%
35264 1
1.9%
45323 1
1.9%
50892 1
1.9%
52553 1
1.9%
58136 1
1.9%
ValueCountFrequency (%)
4375857 1
1.9%
2926093 1
1.9%
2116932 1
1.9%
1963614 1
1.9%
1963496 1
1.9%
1450789 1
1.9%
1419068 1
1.9%
966878 1
1.9%
927577 1
1.9%
832657 1
1.9%

CochesVendidos_2021
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean528352.55
Minimum0
Maximum4232790
Zeros2
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-11-24T19:54:07.788115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16185.2
Q1100373
median230665
Q3429918
95-th percentile2206352.6
Maximum4232790
Range4232790
Interquartile range (IQR)329545

Descriptive statistics

Standard deviation816967.7
Coefficient of variation (CV)1.5462549
Kurtosis8.2614686
Mean528352.55
Median Absolute Deviation (MAD)164309
Skewness2.7196263
Sum28002685
Variance6.6743623 × 1011
MonotonicityNot monotonic
2023-11-24T19:54:07.946381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
3.8%
909983 1
 
1.9%
185092 1
 
1.9%
970626 1
 
1.9%
7673 1
 
1.9%
288920 1
 
1.9%
141467 1
 
1.9%
182274 1
 
1.9%
66356 1
 
1.9%
236044 1
 
1.9%
Other values (42) 42
79.2%
ValueCountFrequency (%)
0 2
3.8%
7673 1
1.9%
21860 1
1.9%
23612 1
1.9%
44019 1
1.9%
44954 1
1.9%
46723 1
1.9%
52978 1
1.9%
53079 1
1.9%
66356 1
1.9%
ValueCountFrequency (%)
4232790 1
1.9%
2627208 1
1.9%
2537021 1
1.9%
1985907 1
1.9%
1982758 1
1.9%
1533662 1
1.9%
1525872 1
1.9%
970626 1
1.9%
967323 1
1.9%
909983 1
1.9%

CochesVendidos_2022
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean522082.49
Minimum0
Maximum4027746
Zeros2
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-11-24T19:54:08.103359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16062.6
Q196754
median226794
Q3451561
95-th percentile2230545.4
Maximum4027746
Range4027746
Interquartile range (IQR)354807

Descriptive statistics

Standard deviation807593.53
Coefficient of variation (CV)1.5468696
Kurtosis7.5870473
Mean522082.49
Median Absolute Deviation (MAD)170071
Skewness2.659788
Sum27670372
Variance6.5220731 × 1011
MonotonicityNot monotonic
2023-11-24T19:54:08.277263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
3.8%
918373 1
 
1.9%
271520 1
 
1.9%
994945 1
 
1.9%
5118 1
 
1.9%
279093 1
 
1.9%
140207 1
 
1.9%
172307 1
 
1.9%
56723 1
 
1.9%
224617 1
 
1.9%
Other values (42) 42
79.2%
ValueCountFrequency (%)
0 2
3.8%
5118 1
1.9%
23359 1
1.9%
27717 1
1.9%
36303 1
1.9%
37439 1
1.9%
41451 1
1.9%
42369 1
1.9%
45371 1
1.9%
49284 1
1.9%
ValueCountFrequency (%)
4027746 1
1.9%
2914485 1
1.9%
2618944 1
1.9%
1971613 1
1.9%
1774157 1
1.9%
1386382 1
1.9%
1352822 1
1.9%
994945 1
1.9%
918373 1
1.9%
908705 1
1.9%

PIB_2017
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27568.859
Minimum1177.083
Maximum111211.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2023-11-24T19:54:08.447288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1177.083
5-th percentile1947.8872
Q19186.712
median20430.567
Q344274.073
95-th percentile72294.863
Maximum111211.77
Range110034.69
Interquartile range (IQR)35087.361

Descriptive statistics

Standard deviation24291.014
Coefficient of variation (CV)0.88110334
Kurtosis1.5192044
Mean27568.859
Median Absolute Deviation (MAD)16545.102
Skewness1.2182026
Sum1461149.5
Variance5.9005334 × 108
MonotonicityNot monotonic
2023-11-24T19:54:08.603407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14618.327 1
 
1.9%
111211.77 1
 
1.9%
9599.362 1
 
1.9%
1177.083 1
 
1.9%
48799.874 1
 
1.9%
42275.793 1
 
1.9%
75940.152 1
 
1.9%
17731.849 1
 
1.9%
1653.406 1
 
1.9%
3153.314 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
1177.083 1
1.9%
1653.406 1
1.9%
1932.763 1
1.9%
1957.97 1
1.9%
2592.707 1
1.9%
2655.99 1
1.9%
2957.899 1
1.9%
3153.314 1
1.9%
3885.465 1
1.9%
6577.287 1
1.9%
ValueCountFrequency (%)
111211.77 1
1.9%
82584.384 1
1.9%
75940.152 1
1.9%
69864.671 1
1.9%
61164.897 1
1.9%
57772.553 1
1.9%
55804.163 1
1.9%
53459.072 1
1.9%
48799.874 1
1.9%
47320.537 1
1.9%

PIB_2018
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29419.159
Minimum1271.677
Maximum117993.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2023-11-24T19:54:08.755657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1271.677
5-th percentile1863.8404
Q19629.603
median23176.358
Q346625.859
95-th percentile80553.151
Maximum117993.37
Range116721.69
Interquartile range (IQR)36996.256

Descriptive statistics

Standard deviation25895.026
Coefficient of variation (CV)0.8802096
Kurtosis1.4879321
Mean29419.159
Median Absolute Deviation (MAD)16673.973
Skewness1.213848
Sum1559215.4
Variance6.7055236 × 108
MonotonicityNot monotonic
2023-11-24T19:54:08.917898image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11786.433 1
 
1.9%
117993.371 1
 
1.9%
10024.115 1
 
1.9%
1271.677 1
 
1.9%
53224.694 1
 
1.9%
42761.93 1
 
1.9%
82605.966 1
 
1.9%
19885.103 1
 
1.9%
1698.034 1
 
1.9%
3279.519 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
1271.677 1
1.9%
1618.967 1
1.9%
1698.034 1
1.9%
1974.378 1
1.9%
2710.158 1
1.9%
3118.258 1
1.9%
3216.254 1
1.9%
3279.519 1
1.9%
3947.248 1
1.9%
6923.64 1
1.9%
ValueCountFrequency (%)
117993.371 1
1.9%
85546.669 1
1.9%
82605.966 1
1.9%
79184.608 1
1.9%
66836.539 1
1.9%
61724.492 1
1.9%
56352.938 1
1.9%
54295.731 1
1.9%
53224.694 1
1.9%
51234.477 1
1.9%

PIB_2019
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28933.166
Minimum1302.277
Maximum113860.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2023-11-24T19:54:09.077662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1302.277
5-th percentile1955.193
Q19750.431
median23333.317
Q346449.962
95-th percentile78027.695
Maximum113860.53
Range112558.26
Interquartile range (IQR)36699.531

Descriptive statistics

Standard deviation25167.17
Coefficient of variation (CV)0.8698381
Kurtosis1.3512273
Mean28933.166
Median Absolute Deviation (MAD)17214.65
Skewness1.18061
Sum1533457.8
Variance6.3338646 × 108
MonotonicityNot monotonic
2023-11-24T19:54:09.364081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10054.023 1
 
1.9%
113860.533 1
 
1.9%
10311.552 1
 
1.9%
1302.277 1
 
1.9%
52672.504 1
 
1.9%
42287.718 1
 
1.9%
76303.683 1
 
1.9%
19069.311 1
 
1.9%
1500.683 1
 
1.9%
3512.195 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
1302.277 1
1.9%
1500.683 1
1.9%
1812.738 1
1.9%
2050.163 1
1.9%
3240.513 1
1.9%
3439.102 1
1.9%
3512.195 1
1.9%
3688.953 1
1.9%
4194.086 1
1.9%
6540.141 1
1.9%
ValueCountFrequency (%)
113860.533 1
1.9%
84474.469 1
1.9%
80613.712 1
1.9%
76303.683 1
1.9%
66070.471 1
1.9%
59678.596 1
1.9%
54289.066 1
1.9%
52672.504 1
1.9%
51694.498 1
1.9%
50195.321 1
1.9%

PIB_2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28099.223
Minimum1376.512
Maximum117616.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2023-11-24T19:54:09.510509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1376.512
5-th percentile1858.4388
Q18571.937
median22224.555
Q344320.723
95-th percentile75279.841
Maximum117616.15
Range116239.64
Interquartile range (IQR)35748.786

Descriptive statistics

Standard deviation25473.647
Coefficient of variation (CV)0.90656053
Kurtosis1.8921115
Mean28099.223
Median Absolute Deviation (MAD)17908.409
Skewness1.3183859
Sum1489258.8
Variance6.4890668 × 108
MonotonicityNot monotonic
2023-11-24T19:54:09.670634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8571.937 1
 
1.9%
117616.151 1
 
1.9%
8770.022 1
 
1.9%
1527.434 1
 
1.9%
52222.364 1
 
1.9%
41307.21 1
 
1.9%
68275.277 1
 
1.9%
17076.319 1
 
1.9%
1376.512 1
 
1.9%
3325.836 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
1376.512 1
1.9%
1527.434 1
1.9%
1776.267 1
1.9%
1913.22 1
1.9%
3325.836 1
1.9%
3548.892 1
1.9%
3780.075 1
1.9%
3802.438 1
1.9%
3932.332 1
1.9%
5363.067 1
1.9%
ValueCountFrequency (%)
117616.151 1
1.9%
86109.53 1
1.9%
85786.688 1
1.9%
68275.277 1
1.9%
61274.006 1
1.9%
60926.877 1
1.9%
53094.491 1
1.9%
52706.294 1
1.9%
52222.364 1
1.9%
49168.162 1
1.9%

PIB_2021
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32438.324
Minimum1216.811
Maximum134925.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2023-11-24T19:54:09.830411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1216.811
5-th percentile2148.4646
Q110106.663
median24703.713
Q351472.103
95-th percentile91804.671
Maximum134925.16
Range133708.35
Interquartile range (IQR)41365.44

Descriptive statistics

Standard deviation29688.557
Coefficient of variation (CV)0.91523093
Kurtosis1.7744474
Mean32438.324
Median Absolute Deviation (MAD)18464.444
Skewness1.3289984
Sum1719231.2
Variance8.8141043 × 108
MonotonicityNot monotonic
2023-11-24T19:54:10.000002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10631.665 1
 
1.9%
134925.164 1
 
1.9%
10177.039 1
 
1.9%
1216.811 1
 
1.9%
58960.71 1
 
1.9%
48792.618 1
 
1.9%
90540.804 1
 
1.9%
19479.401 1
 
1.9%
1565.575 1
 
1.9%
3576.11 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
1216.811 1
1.9%
1565.575 1
1.9%
2013.971 1
1.9%
2238.127 1
1.9%
3576.11 1
1.9%
3753.428 1
1.9%
4145.939 1
1.9%
4362.677 1
1.9%
4874.309 1
1.9%
6239.269 1
1.9%
ValueCountFrequency (%)
134925.164 1
1.9%
101983.636 1
1.9%
93700.472 1
1.9%
90540.804 1
1.9%
77710.07 1
1.9%
69466.552 1
1.9%
63841.724 1
1.9%
61203.119 1
1.9%
58960.71 1
1.9%
53664.908 1
1.9%

PIB_2022
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32910.628
Minimum1227.697
Maximum126598.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2023-11-24T19:54:10.164028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1227.697
5-th percentile2347.1084
Q111265.544
median24540.377
Q349843.163
95-th percentile97518.743
Maximum126598.1
Range125370.41
Interquartile range (IQR)38577.619

Descriptive statistics

Standard deviation29600.073
Coefficient of variation (CV)0.89940772
Kurtosis1.4254175
Mean32910.628
Median Absolute Deviation (MAD)17855.898
Skewness1.3000851
Sum1744263.3
Variance8.7616433 × 108
MonotonicityNot monotonic
2023-11-24T19:54:10.329936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13619.875 1
 
1.9%
126598.103 1
 
1.9%
11265.544 1
 
1.9%
1227.697 1
 
1.9%
57427.756 1
 
1.9%
47226.095 1
 
1.9%
105825.926 1
 
1.9%
23240.138 1
 
1.9%
1650.279 1
 
1.9%
3623.593 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
1227.697 1
1.9%
1650.279 1
1.9%
2279.972 1
1.9%
2391.866 1
1.9%
3623.593 1
1.9%
4086.519 1
1.9%
4587.172 1
1.9%
4606.798 1
1.9%
4798.118 1
1.9%
6658.119 1
1.9%
ValueCountFrequency (%)
126598.103 1
1.9%
105825.926 1
1.9%
103311.007 1
1.9%
93657.234 1
1.9%
82807.649 1
1.9%
68294.907 1
1.9%
64813.854 1
1.9%
57427.756 1
1.9%
56188.324 1
1.9%
55036.52 1
1.9%

Interactions

2023-11-24T19:54:04.066462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:50.264821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:51.396533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:52.677297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:53.895082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:55.118242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:56.309667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:57.647823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:58.997877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:00.243142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:01.517328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:02.773777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:04.170139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:50.358577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:51.488603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:52.780328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:54.019698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:55.215594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:56.418376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:57.749677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:59.102614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:00.409440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:01.621721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:02.882488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:04.275677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:50.450592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:51.583427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:52.875500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:54.120498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:55.313333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:56.526982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:57.850091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:59.203084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:00.509791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:01.728082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:02.986455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:04.384018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:50.542027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:51.675950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:52.971451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:54.218048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:55.410027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:56.636689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:57.951460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:59.305023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:00.610586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:01.831864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:03.094385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:04.485116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:50.630791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:51.764241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:53.086569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:54.310980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:55.503776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:56.743402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:58.050371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:59.401978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:00.708849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:01.934677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:03.195987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:04.586887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:50.718609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:51.851368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:53.177325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:54.405725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:55.594591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:56.854331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:58.145071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:59.498814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:00.804725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:02.033951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:03.298325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:04.699618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:50.822728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:51.964155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:53.288662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:54.514451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:55.707704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:56.975991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:58.259763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:59.618316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:00.918270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:02.151053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:03.420466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:04.801472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:50.911841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:52.056909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:53.386402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:54.611175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:55.802497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:57.084330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:58.359510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:59.717691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:01.017614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:02.253778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:03.524197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:05.037665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:51.010980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:52.153649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:53.485204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:54.711004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:55.904801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:57.197600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:58.458085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:59.821880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:01.114790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:02.357577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:03.633124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:05.137398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:51.103756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:52.376909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:53.582881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:54.809088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:56.000833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:57.308112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:58.697399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:59.921657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:01.212937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:02.457315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:03.735423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:05.239175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:51.198064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:52.472235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:53.683053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:54.910771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:56.103086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:57.421543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:58.791699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:00.025700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:01.308730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:02.560991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:03.847069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:05.345479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:51.300789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:52.580106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:53.792817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:55.016059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:56.206845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:57.537187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:53:58.901040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:00.134828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:01.416121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:02.670126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T19:54:03.954823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-24T19:54:10.446357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
CochesVendidos_2017CochesVendidos_2018CochesVendidos_2019CochesVendidos_2020CochesVendidos_2021CochesVendidos_2022PIB_2017PIB_2018PIB_2019PIB_2020PIB_2021PIB_2022
CochesVendidos_20171.0000.9920.9800.9560.9630.9600.1330.1280.1120.0860.0890.075
CochesVendidos_20180.9921.0000.9910.9690.9590.9540.0720.0670.0520.0280.0300.015
CochesVendidos_20190.9800.9911.0000.9860.9490.9420.0670.0630.0490.0270.0280.013
CochesVendidos_20200.9560.9690.9861.0000.9400.9290.0420.0380.0270.0070.008-0.008
CochesVendidos_20210.9630.9590.9490.9401.0000.9840.0710.0680.0530.0330.0360.022
CochesVendidos_20220.9600.9540.9420.9290.9841.0000.0470.0440.0270.0050.005-0.005
PIB_20170.1330.0720.0670.0420.0710.0471.0000.9980.9950.9900.9920.990
PIB_20180.1280.0670.0630.0380.0680.0440.9981.0000.9980.9940.9940.992
PIB_20190.1120.0520.0490.0270.0530.0270.9950.9981.0000.9970.9970.994
PIB_20200.0860.0280.0270.0070.0330.0050.9900.9940.9971.0000.9980.992
PIB_20210.0890.0300.0280.0080.0360.0050.9920.9940.9970.9981.0000.995
PIB_20220.0750.0150.013-0.0080.022-0.0050.9900.9920.9940.9920.9951.000

Missing values

2023-11-24T19:54:05.499242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-24T19:54:05.724561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryCochesVendidos_2017CochesVendidos_2018CochesVendidos_2019CochesVendidos_2020CochesVendidos_2021CochesVendidos_2022PIB_2017PIB_2018PIB_2019PIB_2020PIB_2021PIB_2022
0Argentina85977077298043679432434035251437863314618.32711786.43310054.0238571.93710631.66513619.875
1Australia1122073107631998303183265790998391837355804.16356352.93854289.06653094.49163841.72464813.854
2Austria37918437031535817527461928607022679447320.53751234.47750195.32148857.08353528.70752191.771
3Belgium57814657926258410046468341630938522544274.07347685.34746783.00545545.23351472.10349843.163
4Brazil21691102475994267923919634961982758197161310419.5769629.6039364.2377344.5298165.6379455.328
5Bulgaria2422330218317262166723612277178414.3899489.6579914.38610170.72312300.18513821.205
6Canada18762961832162177263914190681525872138638245191.99346625.85946449.96243383.71352387.81255036.520
7Chile31764335672031075520733129213228424015003.77015755.00314567.98813067.74216092.14915166.472
8Colombia2219872395932437421711912285492362696577.2876923.6406540.1415363.0676239.2696658.119
9Croatia44264585576170735264440194145113556.31815009.08515089.37314231.06317770.90018305.030
CountryCochesVendidos_2017CochesVendidos_2018CochesVendidos_2019CochesVendidos_2020CochesVendidos_2021CochesVendidos_2022PIB_2017PIB_2018PIB_2019PIB_2020PIB_2021PIB_2022
43Slovenia70304720137185052553529784537123511.31626224.25726138.90125617.86829338.08728526.621
44South Africa5144895062084853293384294081294515616678.2926991.7096622.6295672.2786983.5096684.479
45Spain13725191478681141570996687896732388370128211.92730438.33829603.29826968.37430563.55229799.745
46Sweden34695132962933761526261127481125581153459.07254295.73151694.49852706.29461203.11956188.324
47Switzerland32304530984132408725045725421824009382584.38485546.66984474.46986109.53093700.47293657.234
48Thailand8164649232539341377200296701607755896593.8167298.9417812.8867169.8777227.4767069.589
49Ukraine80489829179090887090103582363032655.9903118.2583688.9533780.0754874.3094606.798
50Uruguay50165416503832832477467234928418626.98118604.95817636.70315198.36817333.60820022.144
51Uzbekistan118879203695267952276378001932.7631618.9671812.7381776.2672013.9712279.972
52Vietnam2083202502552843762645692568403288622957.8993216.2543439.1023548.8923753.4284086.519